Archivio della ricerca - Fondazione Bruno Kessler
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Harnessing biomaterials for advanced biosensor and bioelectronic devices development: From natural chromophores to biodegradable substrates and peptide-based detection of nanoplastics
No full textBiomaterials play a crucial role in advancing biosensor technologies for medical, environmental, and food safety applications. This study investigates natural biomaterials, such as food-derived chromophores, cellulose, and peptides, for high-performance biosensors and bioelectronic devices. Chromophores, namely grape anthocyanins, are potential candidates for the development of artificial retinal devices showing light-responsivity at 435 nm, close to the human blue cone photoreceptors (420 nm), and transient photo-current signals of 15 nA/mm2 (20 ms, blue-light pulse). Realized cellulose-silk fibroin (SF:CNCs)-based biodegradable substrates are suitable for flexible and sustainable optoelectronic devices, showing transmittance over 40% (400 and 800 nm) and good stress at break (60 MPa at 5%). Peptides, derived from enzymes, are used as biorecognition elements in EGOFETs for detecting polystyrene nanoplastics with a sensitivity of 60.3%/(mg/ml). Through the use of chromophores, SF:CNCs-based substrates, and peptide, new biosensors are developed displaying promising applications in biomedicine, green electronics, and environmental pollution-monitoring
Multi-sensor deep learning for change detection
No full textChange detection is an important task in Earth observation, which monitors changes in land cover, land use, and environmental conditions over time. Employing bi-temporal images from remote sensing platforms like satellites, change detection is essential for managing both natural and human-induced transformations. Change detection employs both supervised and unsupervised methods at pixel or object-based scales. Recent trends incorporate deep learning techniques, improving the effectiveness and accuracy of change detection algorithms. Over the past decade, the increase in available remote sensing sensors has led to diverse data sources, including a number of multi-spectral, Synthetic Aperture Radar (SAR), and hyperspectral sensors at different spatial and temporal resolutions. Mentionworthy advancements involve the integration of multi-temporal data from multiple sensors, addressing the trade-off between spatial resolution and temporal frequency. Among other benefits, multi-sensor change detection proves advantageous in regions with frequent cloud cover. Additionally, multi-sensor data offer several other benefits in change detection, e.g., reduced uncertainty in results if used as complementary data sources. However, some unique challenges arise in processing and comparing data from different sensors and many research works have been proposed dedicated to tackle these challenges. This chapter provides a discussion on the research in multi-sensor change detection, initially outlining traditional methods and then emphasizing deep learning approaches. Additionally, it includes a couple of case studies centered around this topic
Advancements in secondary and backscattered electron energy spectra and yields analysis: From theory to applications
No full textOver the past decade, experimental microscopy and spectroscopy have made significant progress in the study of the morphological, optical, electronic and transport properties of materials. These developments include higher spatial resolution, shorter acquisition times, more efficient monochromators and electron analysers, improved contrast imaging and advancements in sample preparation techniques. These advances have driven the need for more accurate theoretical descriptions and predictions of material properties. Computer simulations based on first principles and Monte Carlo methods have emerged as a rapidly growing field for modelling the interaction of charged particles, such as electron, proton and ion beams, with various systems, such as slabs, nanostructures and crystals. This report delves into the theoretical and computational approaches to modelling the physico-chemical mechanisms that occur when charged beams interact with a medium. These mechanisms encompass single and collective electronic excitation, ionisation of the target atoms and the generation of a secondary electron cascade that deposits energy into the irradiated material. We show that the combined application of ab initio methods, which are able to model the dynamics of interacting many-fermion systems, and Monte Carlo methods, which capture statistical fluctuations in energy loss mechanisms by random sampling, proves to be an optimal strategy for the accurate description of charge transport in solids. This joint quantitative approach enables the theoretical interpretation of excitation, loss and secondary electron spectra, the analysis of the chemical composition and dielectric properties of solids and contributes to our understanding of irradiation-induced damage in materials, including those of biological significance
Prevention of Postpartum Depression via a Digital ACT-Based Intervention: Evaluation of a Prototype Using Multiple Case Studies
No full textPostpartum depression (PPD) affects up to 15% of mothers, yet access to preventive psychological interventions during pregnancy remains limited. Acceptance and Commitment Therapy (ACT) has demonstrated efficacy in promoting psychological flexibility and preventing mental distress. Nevertheless, no studies have yet evaluated its use for the prevention of PPD through a chatbot-based digital intervention. The present study describes the development and preliminary evaluation of an ACT-based chatbot intervention (REA) to support women during late pregnancy and the early postpartum period. Nineteen participants interacted with the low-fidelity REA prototype, explored its features, completed two questionnaires, and then participated in semi-structured interviews. Quantitative data were analysed using the Wilcoxon signed-rank test; qualitative data were analysed using thematic analysis. Quantitative analysis revealed significantly elevated scores for the majority of variables, including empathy and listening, fluency, lexicon, clarity, engagement, functionality, aesthetics, information, and perceived impact. The interview findings demonstrated a notable level of appreciation for the intervention. The participants described the chatbot as engaging and supportive, highlighting a smooth interaction flow, content-appropriate language, and messages of suitable length. The REA prototype demonstrated high acceptability, usability, and perceived usefulness among a diverse range of stakeholders, thus supporting its potential as a scalable, stigma-reducing tool for the prevention of PPD. Subsequent research endeavours will focus on refining the chatbot’s personalisation features and conducting comprehensive clinical trials to evaluate its efficacy
Cross-section measurements for the production of a W-boson in association with high-transverse-momentum jets in pp collisions at √s = 13 TeV with the ATLAS detector
No full textFrozen Frontiers: Jesuit Evangelization and Colonial Adaptation in Alaska (1867-1919)
Get PDFThis article examines Jesuit missions in Alaska (1867-1919) through a transnational lens that integrates religious history, colonial studies, and spatial anthropology. It highlights how adaptation to extreme conditions, gendered hierarchies, and the tension between charity and control shaped everyday missionary life. Drawing on archival and published sources, the study frames Alaska as a site of experimental Catholic evangelization, where material frequently superseded doctrinal priorities. It calls for further research in women’s and Indigenous sources to reassess missionary narratives within global Catholic contexts
Computer Science Foundations for Digital Libraries: Algorithms, Systems, and Applications
No full textDigital libraries face challenges in quality, accessibility, and usage of resources. This issue presents seven papers offering computational and technical solutions to these problems: data quality through validation and monitoring, AI evaluation of information systems, and enhanced content discoverability. Research also covers knowledge representation with new provenance models, deep learning for bibliographic control, metadata-driven access to underrepresented languages, and computational methods for restoring historical documents. These papers showcase how modern techniques like machine learning, semantic web technologies, knowledge graphs, and image processing tackle digital library challenges, improving resource quality and accessibility. These papers were selected from the 21st Italian Research Conference on Digital Libraries (IRCDL 2025), held in Udine, Italy, on 20–21 February 2025, which has served since 2005 as a key annual forum bringing together researchers from academia, government, and industry to address topics spanning computer science, digital humanities, information science, librarianship, archival science, museum studies, and cultural heritage
Digital Twins and CFD simulations for accurate sensor positioning
Get PDFBuilding renovation to improve energy efficiency is crucial for reducing CO2 emissions, aligning with the goal of achieving net-zero emissions by 2050. This task requires a holistic approach that encompasses retrofitting outdated systems, enhancing thermal insulation, and integrating renewable energy sources. Simulating different indoor environmental conditions and technological systems within Digital Twin (DT) before interventions is crucial for optimizing energy efficiency. Simulations can support the proper installation of heating and cooling devices and facilitate the deployment of advanced technologies, including smart Heating, Ventilation, and Air Conditioning (HVAC) systems, energy-efficient lighting, and automated energy management solutions. The use of Artificial Intelligence (AI) in simulations allows for the precise sizing of HVAC systems, including heat pumps and related devices, by accurately modelling demand profiles and optimizing sensor placement based on the geometries of DTs.
This study, conducted as part of the Horizon Europe InCUBE project1, explores a real-world use-case at the Centro Servizi Culturali Santa Chiara in Trento, Italy. It introduces an innovative approach that integrates 3D surveying, computational fluid dynamics (CFD), and digital twin (DT) geometries to enhance the analysis of indoor heat distribution. The proposed data-driven pipeline optimizes sensor placement within indoor spaces, ensuring precise system design, improving performance and energy efficiency, and minimizing energy waste while preventing the oversizing of technological systems
PILLAR: LINDDUN Privacy Threat Modeling Using LLMs
No full textThe rapid evolution of Large Language Models (LLMs) has unlocked new possibilities for applying artificial intelligence across a wide range of fields, including privacy engineering. As modern applications increasingly handle sensitive user data, safeguarding privacy has become more critical than ever. To ensure robust data protection, potential threats must be identified and addressed early in the development process. Privacy threat modeling frameworks like LINDDUN offer structured approaches for uncovering these risks, yet they often require significant manual effort, expert knowledge, and detailed system information—making the process time-intensive and reliant on thorough analysis. To address these challenges, we introduce PILLAR (Privacy risk Identification with LINDDUN and LLM Analysis Report), a new tool that implements and automates the LINDDUN framework through LLM integration to streamline and enhance privacy threat modeling. PILLAR automates key parts of the LINDDUN process, such as generating DFDs from unstructured textual inputs (e.g. system descriptions), eliciting privacy threats, and risk-based threat prioritization. By leveraging the capabilities of LLMs, PILLAR can take natural language descriptions of systems and transform them into comprehensive threat models with limited input from users. Furthermore, PILLAR is capable of simulating multi-agent collaboration, allowing different LLM instances to play different contributor roles in a virtual threat modeling workshop. Rather than merely reducing the workload on analysts, PILLAR shifts their involvement from repetitive, tedious tasks to more meaningful and impactful interventions—such as refining the scope of analysis or completing critical components like the DFD. This allows experts to focus on the aspects that truly matter for a robust threat modeling process while enhancing both efficiency and accuracy
Multispacecraft Observations of the 27 Day Periodicity in Galactic Protons from 2018 to 2019
No full textGalactic cosmic-ray (GCR) intensities exhibit recurrent variations caused by their passage through plasma interaction regions corotating with the Sun, with the ∼27 day periodicity being the most prominent one. Data collected by the High-Energy Particle Detector (HEPD-01) on board the China Seismo-Electromagnetic Satellite in Low-Earth Orbit have been used to derive daily proton fluxes from 2018 to 2019 August, in the energy range between ∼55 and ∼200 MeV. Daily fluxes from HEPD-01 have been analyzed along with proton fluxes measured during the same period by ERNE and EPHIN, on board the SOHO spacecraft, and by AMS-02, on board the International Space Station. Using a time-frequency analysis, we confirm a slight energy dependence for the power of the ∼27 day variation as a function of time, with the periodicity maximum occurring earlier for HEPD-01 than for high-energy data from AMS-02. Additionally, as already obtained in previous studies, the rigidity dependence of the amplitude of the aforementioned GCR variation cannot be described by the same power law at both low and high energies, as a consequence of different physical mechanisms playing roles at different rigidity ranges. HEPD-01 GCR measurements cover the energy range from tens to a few hundreds of MeV, which is not accessible to existing detectors (EPHIN and ERNE covering from a few MeV up to tens or a hundred MeV, respectively, and AMS-02 in the GeV–TeV energy range), providing important information for understanding GCR periodicities